A survey of time series forecasting methods based on deep learning

Time series forecasting finds its internal regularity by analyzing time series to forecasts its future. Its research has important academic and application. Especially with the development of sensor and network technology, how to make more accurate prediction and analysis based on a large number of historical time series data has become an urgent problem to be solved. At present, time series forecasting methods fully use the research results of deep learning, and have made rapid development in recent years. This paper analyzes the research status of time series forecasting technology, discusses the relevant theories and methods of deep learning methods involved in time series forecasting of time overview, including the application of convolutional neural network, recurrent neural network, attention mechanism, graph neural network and other methods in the field of time forecasting, and summarizes the research achievements of time series based on deep learning in recent years, The advantages and disadvantages of various time series methods based on deep learning are compared. Finally, this paper forecasts the development trend of time series prediction methods based on deep learning.